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Found 5 papers, 3 papers with code
Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks
The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence.
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains
Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals.
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information.
ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals
The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning.
Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images
The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15. 57s for the OPL-ONL (maximum).
